{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Further Hypothesis Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "── Attaching packages ─────────────────────────────────────── tidyverse 1.3.0 ──\n",
      "✔ ggplot2 3.3.0     ✔ purrr   0.3.4\n",
      "✔ tibble  3.0.1     ✔ dplyr   0.8.5\n",
      "✔ tidyr   1.1.0     ✔ stringr 1.4.0\n",
      "✔ readr   1.3.1     ✔ forcats 0.4.0\n",
      "── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──\n",
      "✖ dplyr::filter() masks stats::filter()\n",
      "✖ dplyr::lag()    masks stats::lag()\n"
     ]
    }
   ],
   "source": [
    "# Select this cell and type Ctrl-Enter to execute the code below.\n",
    "\n",
    "library(tidyverse)\n",
    "\n",
    "set_plot_dimensions <- function(width_choice, height_choice) {\n",
    "    options(repr.plot.width=width_choice, repr.plot.height=height_choice)\n",
    "}\n",
    "\n",
    "cbPal <- c(\"#E69F00\", \"#56B4E9\", \"#009E73\", \"#F0E442\", \"#CC79A7\", \"#0072B2\", \"#D55E00\")\n",
    "\n",
    "set_plot_dimensions(5, 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# You should see \"Attaching packages\" and some ticks by the packages loaded.\n",
    "# The \"Conflicts\" aren't a problem.\n",
    "\n",
    "# Other problems loading the library? Try running this cell.\n",
    "\n",
    "install.packages('tidyverse')\n",
    "\n",
    "library(tidyverse)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1 - Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this workshop, you will apply a number of commonly encountered parametric and non-parametric tests to answer a variety of research questions about an example data set.\n",
    "\n",
    "We begin with a brief exploration of the data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data set"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The file `stars.csv` contains a dataset of 240 stars, with five variables for each star:\n",
    "\n",
    "|variable | description|\n",
    "|:---|:---|\n",
    "|temperature | the surface temperature (K)|\n",
    "|luminosity | luminosity relative to sun|\n",
    "|radius | radius relative to sun|\n",
    "|spectral_class | the spectral class of each star (O,B,A,F,G,K or M)|\n",
    "|type | as defined below|\n",
    "\n",
    "\n",
    "The luminosity and radius of each star is calculated relative to that of the Sun:\n",
    "\n",
    "$L_{sun} = 3.83 \\times 10^{26}\\text{W}$\n",
    "\n",
    "$R_{sun} = 6.96 \\times 10^8\\text{m}$\n",
    "\n",
    "\n",
    "The stars are classified into 6 types:\n",
    "\n",
    "code | type\n",
    ":---|:---\n",
    "0 |Brown Dwarf\n",
    "1 |Red Dwarf\n",
    "2 |White Dwarf\n",
    "3 |Main Sequence\n",
    "4 |Supergiant\n",
    "5 |Hypergiant\n",
    "\n",
    "The dataset contains 40 examples of each type.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the data using the `read_csv` function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Parsed with column specification:\n",
      "cols(\n",
      "  temperature = col_double(),\n",
      "  luminosity = col_double(),\n",
      "  radius = col_double(),\n",
      "  spectral_class = col_character(),\n",
      "  type = col_double()\n",
      ")\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>temperature</th><th scope=col>luminosity</th><th scope=col>radius</th><th scope=col>spectral_class</th><th scope=col>type</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>3068    </td><td>0.002400</td><td>0.1700  </td><td>M       </td><td>0       </td></tr>\n",
       "\t<tr><td>3042    </td><td>0.000500</td><td>0.1542  </td><td>M       </td><td>0       </td></tr>\n",
       "\t<tr><td>2600    </td><td>0.000300</td><td>0.1020  </td><td>M       </td><td>0       </td></tr>\n",
       "\t<tr><td>2800    </td><td>0.000200</td><td>0.1600  </td><td>M       </td><td>0       </td></tr>\n",
       "\t<tr><td>1939    </td><td>0.000138</td><td>0.1030  </td><td>M       </td><td>0       </td></tr>\n",
       "\t<tr><td>2840    </td><td>0.000650</td><td>0.1100  </td><td>M       </td><td>0       </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lllll}\n",
       " temperature & luminosity & radius & spectral\\_class & type\\\\\n",
       "\\hline\n",
       "\t 3068     & 0.002400 & 0.1700   & M        & 0       \\\\\n",
       "\t 3042     & 0.000500 & 0.1542   & M        & 0       \\\\\n",
       "\t 2600     & 0.000300 & 0.1020   & M        & 0       \\\\\n",
       "\t 2800     & 0.000200 & 0.1600   & M        & 0       \\\\\n",
       "\t 1939     & 0.000138 & 0.1030   & M        & 0       \\\\\n",
       "\t 2840     & 0.000650 & 0.1100   & M        & 0       \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| temperature | luminosity | radius | spectral_class | type |\n",
       "|---|---|---|---|---|\n",
       "| 3068     | 0.002400 | 0.1700   | M        | 0        |\n",
       "| 3042     | 0.000500 | 0.1542   | M        | 0        |\n",
       "| 2600     | 0.000300 | 0.1020   | M        | 0        |\n",
       "| 2800     | 0.000200 | 0.1600   | M        | 0        |\n",
       "| 1939     | 0.000138 | 0.1030   | M        | 0        |\n",
       "| 2840     | 0.000650 | 0.1100   | M        | 0        |\n",
       "\n"
      ],
      "text/plain": [
       "  temperature luminosity radius spectral_class type\n",
       "1 3068        0.002400   0.1700 M              0   \n",
       "2 3042        0.000500   0.1542 M              0   \n",
       "3 2600        0.000300   0.1020 M              0   \n",
       "4 2800        0.000200   0.1600 M              0   \n",
       "5 1939        0.000138   0.1030 M              0   \n",
       "6 2840        0.000650   0.1100 M              0   "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data <- read_csv(\"stars.csv\")\n",
    "\n",
    "type_key <- c('Brown Dwarf', 'Red Dwarf', 'White Dwarf', 'Main Sequence', 'Supergiant','Hypergiant')\n",
    "spectral_classes <- c('O','B','A','F','G','K','M')\n",
    "\n",
    "data$type <- factor(data$type)\n",
    "data$spectral_class <- factor(data$spectral_class, levels=spectral_classes)\n",
    "\n",
    "head(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will be using `ggplot2` to visualise distributions of these variables.\n",
    "\n",
    "For example, execute the following to see an overall histogram of luminosity:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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7daP9wFKxvnuoJ5aImPqh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\nP++bXvOjFqE4AAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ggplot(data, aes(x=luminosity)) + \n",
    "    geom_histogram(bins=50, alpha=0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or, more usefully, on a log scale:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Q+kuEUUCkjAgggAACCCCA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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ggplot(data, aes(x=log(luminosity))) + \n",
    "    geom_histogram(bins=50, alpha=0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can split the data by another variable to compare different groups of stars. \n",
    "\n",
    "For example, the following box plot shows log(luminosity), grouped by type:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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EvKjAACCCCAAAIIGAFLAywtgd5Y9Ne//rWsXbtWKioq+JocLkwEEEAAAQQQcLyA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\nJnXJGwEEEEAAAQRyUuD/A7I2WYVp3PDvAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ggplot(data, aes(x=type, y=log(luminosity), fill=type)) + \n",
    "    scale_fill_manual(values=cbPal) +\n",
    "    geom_boxplot(alpha=0.5) + \n",
    "    guides(fill=\"none\") +\n",
    "    coord_flip()                                          \n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "R",
   "language": "R",
   "name": "ir"
  },
  "language_info": {
   "codemirror_mode": "r",
   "file_extension": ".r",
   "mimetype": "text/x-r-source",
   "name": "R",
   "pygments_lexer": "r",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
